Application of spectral reflectance for increasing plant discrimination speed in precision agriculture
2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT)
Institute of Electrical and Electronics Engineers (IEEE)
School of Science / Electron Science Research Institute
Increasing the speed of weed/crop discrimination sensor engines is an increasingly challenging research area in precision agriculture (PA). Data collection, modelling, and real-Time operation are currently the major challenges for accurate plant classification and effective weed control. In the current study, a new low-resolution spectral reflectance sensing is proposed for data collection and applied in conjunction with state-of-Art convolutional neural network (CNN) algorithm for real-Time weed detection. Experimental results demonstrate that the speed of the algorithm is ten times faster than typical spatial imaging based counterparts, while its discrimination accuracy is almost the same.